The role of artificial intelligence in anti-money laundering detecting repetitive and suspicious transaction patterns

Janpreet Singh , Puspal Mondal , Garvit Nayyar , Ashish Shrivastava , Shruti Abhijeet Ballal

Artificial Intelligence, Anti-Money Laundering, Machine Learning, Suspicious Transactions, Financial Crime Detection

Anti-Money Laundering has become a critical function for financial institutions due to the rapid expansion of digital transactions, increased globalization of financial systems, and the growing sophistication of financial crimes. The widespread use of online banking platforms, mobile payment systems, fintech services, and digital financial channels has significantly increased transaction volumes and complexity, making the detection of illicit financial activities more challenging. Traditional rule-based AML systems rely on predefined rules, static thresholds, and manual intervention to monitor transactions. While these systems provide a foundational level of compliance, they often struggle to detect advanced money laundering techniques that continuously evolve over time. Consequently, such systems generate a high number of false-positive alerts, increasing operational costs and placing a heavy burden on compliance teams, while still failing to identify genuinely suspicious activities effectively. Artificial Intelligence has emerged as a powerful and transformative tool for enhancing AML systems by enabling intelligent, data-driven analysis of large volumes of transaction data. AI-based models can learn from historical transaction behavior, identify repetitive transaction structures, detect abnormal patterns, and adapt to changing laundering techniques. This capability allows financial institutions to improve the accuracy and efficiency of suspicious transaction detection while minimizing unnecessary alerts. This paper examines the role of AI in AML with a specific focus on detecting repetitive transactions and anomalous behavioral patterns commonly associated with money laundering activities. The study is based entirely on secondary data obtained from academic literature, industry reports, and regulatory publications, ensuring a comprehensive and reliable analysis. The paper highlights key AI techniques such as machine learning, anomaly detection, and network analysis, and discusses their benefits, limitations, and future potential in strengthening AML frameworks and enhancing financial crime prevention mechanisms.

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"The role of artificial intelligence in anti-money laundering detecting repetitive and suspicious transaction patterns", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a908-a914, January-2026, Available :https://ijsdr.org/papers/IJSDR2601097.pdf

Volume 11 Issue 1, January-2026

Pages : a908-a914

Paper Reg. ID: IJSDR_306937

Published Paper Id: IJSDR2601097

Downloads: 000304

Research Area: Science and Technology

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2601097

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2601097

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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